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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sl-93/ECG_SSCL/blob/main/ECG_DL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hello**\n",
        "\n",
        "**In this notebook, I'll show you the implementation of CNN-Contrastive Model on MIT-BIH data.**\n",
        "\n",
        "Let's kick off! :)"
      ],
      "metadata": {
        "id": "gAcVoHXzUr5c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.  What is the intuition behind Self-Supervised Contrastive Learning(SSCL)?\n",
        "![Screenshot 2022-11-24 230134.jpg]()\n",
        "\n",
        "Well, as you see in the picture above, it is a framework for SSCL. in this framework, the training proccess is done with unlabeled data.\n",
        "\n",
        "Let's check it step by step:\n",
        "\n",
        "1.   The data is augmented and this leads to generation of two views **xi** and **xj**.\n",
        "2.   An encoder(here is a CNN) gets **xi** and **xj** and produces **hi** and **hj** as its output.\n",
        "3.   **hi** and **hj** are mapped into latent space with a linear layer and two vectors **zi** and **zj** are generated.\n",
        "4.   At the end, the agreement between **zi** and **zj** is increased by the formula below:\n",
        "\n",
        "![image.png]()\n",
        "\n",
        "Where sim(.,.) is Cosine similarity. \n",
        "\n",
        "In order to train, each batch of unlabeled data with a size of N is augmented to produce 2N samples and then the Contrastive loss between the latent representations corresponding to **xi** and **xj** which are considered as a positive pair is calculated with the formula above.\n",
        "\n",
        "I'd like to mention that, I'm going to use the Train set for training the encoder and the Test set for fine-tuning the model.\n"
      ],
      "metadata": {
        "id": "C86anCzqDfPV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Let's get more familiar with the data!\n",
        "\n",
        "**Heads-up**: I read the data from my Drive so it's not accessible for you as a reader!"
      ],
      "metadata": {
        "id": "OVBSDfFhV1_n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# read data\n",
        "train_df = pd.read_csv(\"/content/drive/MyDrive/ECG-data/mitbih_train.csv\",header = None)\n",
        "test_df = pd.read_csv(\"/content/drive/MyDrive/ECG-data/mitbih_test.csv\",header = None)"
      ],
      "metadata": {
        "id": "JyaSEXkoVTon"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9Ps6Ij_PcTOv",
        "outputId": "7fc0d251-31f4-4245-a705-0ca123657405"
      },
      "execution_count": null,
      "outputs": [
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          "metadata": {},
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      ]
    },
    {
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        "As you see, the Train file consists of 87554 records with 187 features. The last column is associated with the class name. This dataset has 5 different classes. All features has been normalized between 0 and 1.\n",
        "\n",
        "**We know that each record represents one heart beat with the sample rate of 125.**"
      ],
      "metadata": {
        "id": "ch5zQxaJdVDb"
      }
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb1b6a3f-9688-4cb7-b1bb-8f5fc78e8cc5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cb1b6a3f-9688-4cb7-b1bb-8f5fc78e8cc5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And Test file is made up of 21892 records."
      ],
      "metadata": {
        "id": "P26EnsBOfrRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split x and y\n",
        "x_train = train_df.iloc[:,:-1]\n",
        "y_train = train_df.iloc[:,-1]\n",
        "x_test = test_df.iloc[:,:-1]\n",
        "y_test = test_df.iloc[:,-1]"
      ],
      "metadata": {
        "id": "9h4_D0Y1fpuQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot 20 records of each class!"
      ],
      "metadata": {
        "id": "-_gUy3HxlPjI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "f, ax = plt.subplots(5, sharex=True, sharey=True, figsize=(10,10))\n",
        "for i in range(5):\n",
        "  for j in range(20):\n",
        "    ax[i].set_title(\"class{}\".format(i))\n",
        "    ax[i].plot(x_train[y_train == i].iloc[j,:], color=\"blue\", alpha=.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "dtHDxRtelgEN",
        "outputId": "7a553f1e-f35f-42b8-ed5f-60683bdfd0e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It seems that each class has the unique pattern of beat through time!"
      ],
      "metadata": {
        "id": "wPWMhKg4mL91"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "And the distribution of the samples across the classes:"
      ],
      "metadata": {
        "id": "9Xr2gLCHmpG_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts().plot(kind=\"bar\", title=\"class\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "3cDF1lcpmn7Y",
        "outputId": "1c689d1f-888a-4130-f940-85375cd10237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f464ed70a90>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts().plot(kind=\"bar\", title=\"class\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "IqqcLLSpohbn",
        "outputId": "5caac2cd-0fa8-446f-f131-17b1f76fe3ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f464ecfe5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Data is highly unbalanced!**\n",
        "\n",
        "It's time to take advantage of Deep Learning to classify the data..."
      ],
      "metadata": {
        "id": "0qKH0OtKnGgr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. We'll create dataloader and use Masking method for data augmentation.\n",
        "\n",
        "For augmentation I'll use Masking method. Predefined percentage of input pattern features are randomly masked. This parameter is adjustable in its function, but I set it to 10% by default. As I mentioned earlier, the Train set is used to train the encoder.\n",
        "\n",
        "It's mentionable that I'll use 95% and 5% for training and evaluation respectively."
      ],
      "metadata": {
        "id": "-PqTcdDHstjf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# a function for masking the data\n",
        "import numpy as np\n",
        "\n",
        "def masking(x_data, percentage):\n",
        "  for i in range(len(x_data)):\n",
        "    indices = np.random.choice(np.arange(x_data[i].size), replace=False,\n",
        "                           size=int(x_data[i].size * percentage))\n",
        "    x_data[i][indices] = 0\n",
        "  return x_data"
      ],
      "metadata": {
        "id": "90FnR4L8mCbr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# a function for creating dataloader\n",
        "from torch import Tensor\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def Create_Dataloader(train_data, batch_size, percentage):\n",
        "\n",
        "  # read the data\n",
        "  train_ds = pd.read_csv(train_data, header = None)\n",
        "\n",
        "  # shuffle the data\n",
        "  train_ds = train_ds.sample(frac = 1)\n",
        "\n",
        "  # drop label\n",
        "  x_train = train_ds.iloc[:,:-1]\n",
        "  y_train = train_ds.iloc[:,-1]\n",
        "\n",
        "  # train, valid, test split\n",
        "  x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=.05, random_state=42)\n",
        "  x_train = np.array(x_train)\n",
        "  x_valid = np.array(x_valid)\n",
        "\n",
        "  # masking\n",
        "  trainX1 = masking(x_train, percentage)\n",
        "  trainX2 = masking(x_train, percentage)\n",
        "  validX1 = masking(x_valid, percentage)\n",
        "  validX2 = masking(x_valid, percentage)\n",
        "\n",
        "  # reshape the data\n",
        "  X_train1 = np.reshape(trainX1, (trainX1.shape[0], trainX1.shape[1], 1))\n",
        "  X_train2 = np.reshape(trainX2, (trainX2.shape[0], trainX2.shape[1], 1))\n",
        "  X_valid1 = np.reshape(validX1, (validX1.shape[0], validX1.shape[1], 1))\n",
        "  X_valid2 = np.reshape(validX2, (validX2.shape[0], validX2.shape[1], 1))\n",
        "\n",
        "  # Create the DataLoader for the data\n",
        "  X_train1 = torch.tensor(X_train1)\n",
        "  X_train2 = torch.tensor(X_train2)\n",
        "  X_valid1 = torch.tensor(X_valid1)\n",
        "  X_valid2 = torch.tensor(X_valid2)\n",
        "\n",
        "  train_data = TensorDataset(X_train1, X_train2)\n",
        "  valid_data = TensorDataset(X_valid1, X_valid2)\n",
        "\n",
        "  train_sampler = RandomSampler(train_data)\n",
        "  valid_sampler = RandomSampler(valid_data)\n",
        "\n",
        "  train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
        "  valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=batch_size)\n",
        "\n",
        "  return train_dataloader, valid_dataloader\n"
      ],
      "metadata": {
        "id": "PpoNTYDqv-g0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Defining the model's architecture and training.\n",
        "\n",
        "As I mentioned earlier, we need an encoder to generate latent representations of data. I use CNN as an encoder which is made up of 5 convolution layers and 1 linear layer. "
      ],
      "metadata": {
        "id": "jMy1pbvUB2cq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model architecture\n",
        "import torch.nn as nn\n",
        "\n",
        "class CNN_Model(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNN_Model, self).__init__()\n",
        "        \n",
        "        # 187 x 1\n",
        "        self.conv1 = nn.Sequential(\n",
        "            nn.Conv1d(1, 32, kernel_size=2, stride=1, padding=0),\n",
        "            nn.BatchNorm1d(32),\n",
        "            nn.ReLU())\n",
        "        \n",
        "        # 186 x 32\n",
        "        self.conv2 = nn.Sequential(\n",
        "            nn.Conv1d(32, 64, kernel_size=2, stride=2, padding=0),\n",
        "            nn.BatchNorm1d(64),\n",
        "            nn.ReLU())\n",
        "        \n",
        "        # 93 x 64\n",
        "        self.conv3 = nn.Sequential(\n",
        "            nn.Conv1d(64, 128, kernel_size=2, stride=1, padding=0),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool1d(4, stride=4))\n",
        "        \n",
        "        # 23 x 128\n",
        "        self.conv4 = nn.Sequential(\n",
        "            nn.Conv1d(128, 256, kernel_size=3, stride=1, padding=0),\n",
        "            nn.BatchNorm1d(256),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool1d(3, stride=3))\n",
        "        \n",
        "        # 7 x 256\n",
        "        self.conv5 = nn.Sequential(\n",
        "            nn.Conv1d(256, 512, kernel_size=3, stride=1, padding=0),\n",
        "            nn.BatchNorm1d(512),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool1d(5, stride=5))\n",
        "        \n",
        "        # 1 x 512\n",
        "        self.projection = nn.Linear(512, 256)\n",
        "\n",
        "    \n",
        "    def forward(self, x):\n",
        "        # expected conv1d input : minibatch_size x num_channel x width\n",
        "        x = x.view(x.shape[0], 1,-1)\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.conv2(out)\n",
        "        out = self.conv3(out)\n",
        "        out = self.conv4(out)\n",
        "        out = self.conv5(out)\n",
        "\n",
        "        out_f = out.view(x.shape[0], out.size(1) * out.size(2))\n",
        "        logit = self.projection(out_f)\n",
        "        \n",
        "        return logit"
      ],
      "metadata": {
        "id": "ye6sB1LaB_tt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# A class for contrastive loss\n",
        "class ContrastiveLoss(nn.Module):\n",
        "    def __init__(self, temperature, batch_size):\n",
        "        super(ContrastiveLoss, self).__init__()\n",
        "        self.batch_size = batch_size\n",
        "        self.temperature = temperature\n",
        "\n",
        "        self.mask = self.mask_correlated_samples(batch_size)\n",
        "        self.criterion = nn.CrossEntropyLoss(reduction=\"sum\")\n",
        "        self.similarity_f = nn.CosineSimilarity(dim=2)\n",
        "\n",
        "    def mask_correlated_samples(self, batch_size):\n",
        "        N = 2 * batch_size\n",
        "        mask = torch.ones((N, N), dtype=bool)\n",
        "        mask = mask.fill_diagonal_(0)\n",
        "        \n",
        "        for i in range(batch_size):\n",
        "            mask[i, batch_size + i] = 0\n",
        "            mask[batch_size + i, i] = 0\n",
        "        return mask\n",
        "\n",
        "    def forward(self, z_i, z_j):\n",
        "\n",
        "        N = 2 * self.batch_size\n",
        "\n",
        "        z = torch.cat((z_i, z_j), dim=0)\n",
        "\n",
        "        sim = self.similarity_f(z.unsqueeze(1), z.unsqueeze(0)) / self.temperature\n",
        "\n",
        "        sim_i_j = torch.diag(sim, self.batch_size)\n",
        "        sim_j_i = torch.diag(sim, -self.batch_size)\n",
        "        \n",
        "        # We have 2N samples, but with Distributed training every GPU gets N examples too, resulting in: 2xNxN\n",
        "        positive_samples = torch.cat((sim_i_j, sim_j_i), dim=0).reshape(N, 1)\n",
        "        negative_samples = sim[self.mask].reshape(N, -1)\n",
        "        \n",
        "        labels = torch.from_numpy(np.array([0]*N)).reshape(-1).to(positive_samples.device).long()\n",
        "        \n",
        "        logits = torch.cat((positive_samples, negative_samples), dim=1)\n",
        "        loss = self.criterion(logits, labels)\n",
        "        loss /= N\n",
        "        \n",
        "        return loss"
      ],
      "metadata": {
        "id": "BgwLQicHt-rj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if torch.cuda.is_available():       \n",
        "    device = torch.device(\"cuda\")\n",
        "    print(f'There are {torch.cuda.device_count()} GPU(s) available.')\n",
        "    print('Device name:', torch.cuda.get_device_name(0))\n",
        "\n",
        "else:\n",
        "    print('No GPU available, using the CPU instead.')\n",
        "    device = torch.device(\"cpu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVYNee2GuK9m",
        "outputId": "622ca3ea-07b3-472b-d97d-fd27fa4a3f19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1 GPU(s) available.\n",
            "Device name: Tesla T4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Classifier and the optimizer.\n",
        "def initialize_model():\n",
        "\n",
        "    # Instantiate model\n",
        "    model_classifier = CNN_Model()\n",
        "\n",
        "    # Tell PyTorch to run the model on GPU\n",
        "    model_classifier.to(device)\n",
        "\n",
        "    # Create the optimizer\n",
        "    optimizer = torch.optim.AdamW(model_classifier.parameters(), lr=0.0002, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)\n",
        "    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)\n",
        "\n",
        "    return model_classifier, optimizer, scheduler"
      ],
      "metadata": {
        "id": "aKhIYEr5uOm8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# After the completion of each training epoch, measure the model's performance on our validation set.\n",
        "def evaluate(model, val_dataloader, tem):\n",
        "\n",
        "    val_loss = 0\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        val_loss_epoch = 0\n",
        "        \n",
        "        for step, (x_i, x_j) in enumerate(val_dataloader):\n",
        "          criterion = ContrastiveLoss(batch_size = len(x_i), temperature = 0.5)\n",
        "          x_i = x_i.squeeze().to('cuda:0').float()\n",
        "          x_j = x_j.squeeze().to('cuda:0').float()\n",
        "\n",
        "          # positive pair, with encoding\n",
        "          z_i = model(x_i)\n",
        "          z_j = model(x_j)\n",
        "\n",
        "          val_loss += criterion(z_i, z_j).cpu()\n",
        "\n",
        "    # Compute the average loss over the validation set.\n",
        "    val_loss = val_loss / len(val_dataloader)\n",
        "\n",
        "    return val_loss"
      ],
      "metadata": {
        "id": "HWieiGvauarl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import time\n",
        "\n",
        "# A function for training\n",
        "def train(model, optimizer,scheduler, temperature, ep, train_dataloader, val_dataloader=None, evaluation=False):\n",
        "\n",
        "    # Start training loop\n",
        "    print(\"Start training...\\n\")\n",
        "    val_list = []\n",
        "    train_list = []\n",
        "    best_val_loss = float('inf')\n",
        "    # Print the header of the result table\n",
        "    print(f\"{'Epoch':^7} | {'Train Loss':^12} | {'Val Loss':^10} | {'Elapsed':^9}\")\n",
        "    print(\"-\"*47)\n",
        "\n",
        "    for e in range(1,ep + 1):\n",
        "        # =======================================\n",
        "        #               Training\n",
        "        # =======================================\n",
        "\n",
        "        # Measure the elapsed time of each epoch\n",
        "        t0_epoch, t0_batch = time.time(), time.time()\n",
        "\n",
        "        # Reset tracking variables at the beginning of each epoch\n",
        "        total_loss, batch_loss, batch_counts = 0, 0, 0\n",
        "\n",
        "        # Put the model into the training mode\n",
        "        model.train()\n",
        "        \n",
        "        # For each batch of training data...\n",
        "        train_accuracy = []\n",
        "        for batch, (x_i, x_j) in enumerate(train_dataloader):\n",
        "            criterion = ContrastiveLoss(batch_size = len(x_i), temperature = 0.5)\n",
        "            batch_counts +=1\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # Load batch to GPU\n",
        "            x_i = x_i.squeeze().to('cuda:0').float()\n",
        "            x_j = x_j.squeeze().to('cuda:0').float()\n",
        "\n",
        "            # positive pair, with encoding\n",
        "            z_i = model(x_i)\n",
        "            z_j = model(x_j)\n",
        "\n",
        "            # calculate loss\n",
        "            loss = criterion(z_i, z_j)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # Zero out any previously calculated gradients\n",
        "            model.zero_grad()\n",
        "\n",
        "            batch_loss += loss.item()\n",
        "            total_loss += loss.item()\n",
        "\n",
        "            # Clip the norm of the gradients to 1.0 to prevent \"exploding gradients\"\n",
        "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "\n",
        "        # Reset batch tracking variables\n",
        "        batch_loss, batch_counts = 0, 0\n",
        "        t0_batch = time.time()\n",
        "        scheduler.step()\n",
        "\n",
        "        # Calculate the average loss over the entire training data\n",
        "        avg_train_loss = total_loss / len(train_dataloader)\n",
        "        train_list.append(avg_train_loss)\n",
        "\n",
        "        # =======================================\n",
        "        #               Evaluation\n",
        "        # =======================================\n",
        "        if evaluation == True:\n",
        "            # After the completion of each training epoch, measure the model's performance\n",
        "            # on our validation set.\n",
        "            val_loss = evaluate(model, val_dataloader, temperature)\n",
        "            val_list.append(val_loss)\n",
        "\n",
        "            time_elapsed = time.time() - t0_epoch\n",
        "\n",
        "  \n",
        "            # print result table\n",
        "            print(f\"{e:^7} | {avg_train_loss:^12.6f} | {val_loss:^10.6f} | {time_elapsed:^9.2f}\")\n",
        "            print(\"-\"*47)\n",
        "        #print(\"\\n\")\n",
        "    torch.save(model.state_dict(), '/content/pre_model.pt')\n",
        "    \n",
        "    print(\"\\n\")\n",
        "    # plot train and valid loss\n",
        "    plt.plot(list(range(len(val_list))), val_list, label = \"validation loss\")\n",
        "    plt.plot(list(range(len(train_list))), train_list, label = \"training loss\")\n",
        "    plt.title('loss')\n",
        "    plt.xlabel('number of epochs')\n",
        "    plt.ylabel('loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    print(\"\\n\")\n",
        "    \n",
        "    print(\"Training complete!\")"
      ],
      "metadata": {
        "id": "1XvpA3UEusj1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# A function for setting seed\n",
        "def set_seed(seed_value):\n",
        "\n",
        "    random.seed(seed_value)\n",
        "    np.random.seed(seed_value)\n",
        "    torch.manual_seed(seed_value)\n",
        "    torch.cuda.manual_seed_all(seed_value)"
      ],
      "metadata": {
        "id": "mEPW0m0m5SXA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set seed for reproducibility\n",
        "SEED = 42\n",
        "set_seed(SEED)\n",
        "# initialize model\n",
        "model, optimizer, scheduler = initialize_model()"
      ],
      "metadata": {
        "id": "toqtoGKnxwoW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_path = \"/content/drive/MyDrive/ECG-data/mitbih_train.csv\"\n",
        "batch_size = 64\n",
        "percentage = 0.1\n",
        "train_dataloader, val_dataloader = Create_Dataloader(train_path, batch_size, percentage)"
      ],
      "metadata": {
        "id": "_OZ3L1Gb0L_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training pretrained model\n",
        "temprature = 0.5\n",
        "epoch = 30\n",
        "train(model, optimizer, scheduler, temprature, epoch, train_dataloader, val_dataloader, evaluation=True)"
      ],
      "metadata": {
        "id": "UAgsg2oH0kls",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f5853000-bd1a-4adc-def0-7e4bed02c7db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start training...\n",
            "\n",
            " Epoch  |  Train Loss  |  Val Loss  |  Elapsed \n",
            "-----------------------------------------------\n",
            "   1    |   2.906650   |  2.950559  |   25.14  \n",
            "-----------------------------------------------\n",
            "   2    |   2.890989   |  2.924983  |   23.05  \n",
            "-----------------------------------------------\n",
            "   3    |   2.888429   |  2.925665  |   22.94  \n",
            "-----------------------------------------------\n",
            "   4    |   2.886596   |  2.915095  |   22.84  \n",
            "-----------------------------------------------\n",
            "   5    |   2.885274   |  2.914587  |   25.45  \n",
            "-----------------------------------------------\n",
            "   6    |   2.884093   |  2.911564  |   24.10  \n",
            "-----------------------------------------------\n",
            "   7    |   2.883039   |  2.909435  |   23.07  \n",
            "-----------------------------------------------\n",
            "   8    |   2.882301   |  2.903819  |   23.11  \n",
            "-----------------------------------------------\n",
            "   9    |   2.881572   |  2.900838  |   23.12  \n",
            "-----------------------------------------------\n",
            "  10    |   2.880906   |  2.900629  |   23.08  \n",
            "-----------------------------------------------\n",
            "  11    |   2.880465   |  2.900759  |   23.14  \n",
            "-----------------------------------------------\n",
            "  12    |   2.879941   |  2.901128  |   22.94  \n",
            "-----------------------------------------------\n",
            "  13    |   2.879464   |  2.898990  |   22.91  \n",
            "-----------------------------------------------\n",
            "  14    |   2.879096   |  2.897514  |   22.83  \n",
            "-----------------------------------------------\n",
            "  15    |   2.878783   |  2.896898  |   22.82  \n",
            "-----------------------------------------------\n",
            "  16    |   2.878467   |  2.896651  |   22.98  \n",
            "-----------------------------------------------\n",
            "  17    |   2.878203   |  2.895259  |   24.67  \n",
            "-----------------------------------------------\n",
            "  18    |   2.877965   |  2.897673  |   22.84  \n",
            "-----------------------------------------------\n",
            "  19    |   2.877749   |  2.897102  |   22.90  \n",
            "-----------------------------------------------\n",
            "  20    |   2.877558   |  2.895641  |   22.84  \n",
            "-----------------------------------------------\n",
            "  21    |   2.877383   |  2.895856  |   22.88  \n",
            "-----------------------------------------------\n",
            "  22    |   2.877226   |  2.894312  |   22.90  \n",
            "-----------------------------------------------\n",
            "  23    |   2.877068   |  2.894649  |   22.83  \n",
            "-----------------------------------------------\n",
            "  24    |   2.876954   |  2.895339  |   22.89  \n",
            "-----------------------------------------------\n",
            "  25    |   2.876825   |  2.894686  |   22.84  \n",
            "-----------------------------------------------\n",
            "  26    |   2.876693   |  2.894059  |   22.86  \n",
            "-----------------------------------------------\n",
            "  27    |   2.876656   |  2.893733  |   22.85  \n",
            "-----------------------------------------------\n",
            "  28    |   2.876553   |  2.894478  |   22.77  \n",
            "-----------------------------------------------\n",
            "  29    |   2.876487   |  2.894231  |   24.80  \n",
            "-----------------------------------------------\n",
            "  30    |   2.876404   |  2.894260  |   22.86  \n",
            "-----------------------------------------------\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Training complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the result of training the encoder after 30 epochs."
      ],
      "metadata": {
        "id": "X94M4WCrnw8B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load pretrained model and tune for classification\n",
        "model = CNN_Model()\n",
        "model.load_state_dict(torch.load('/content/pre_model.pt'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euh9cq3P60DI",
        "outputId": "fcc4137d-fbf7-4b5e-d4ec-747dfc164cbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Evaluating the trined encoder\n",
        "\n",
        "In order to evaluate the encoder I'm going to use TSNE plot. This plot will tell us that how good the encoder can be in seperating the data. I'll randomly use some samples of the Test set for this pupose."
      ],
      "metadata": {
        "id": "-6eAEmujstEM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# A function for plotting TSNE\n",
        "def Plot_TSNE(test_df, num):\n",
        "\n",
        "  a = pd.DataFrame()\n",
        "  test_df = test_df.sample(frac = 1)\n",
        "  x_test = test_df.iloc[:,:-1]\n",
        "  y_test = test_df.iloc[:,-1]\n",
        "  for i in range(num):\n",
        "    x = np.array(x_test.iloc[i, :])\n",
        "    x = np.reshape(x, (1, x.shape[0], 1))\n",
        "    x = torch.tensor(x)\n",
        "    y = model(x.float())\n",
        "    a = pd.concat([a,pd.DataFrame(y.cpu().detach().numpy())])\n",
        "  X = a\n",
        "  y = y_test[:num].values.reshape(-1,).tolist()\n",
        "\n",
        "  tsne = TSNE(n_components=2, random_state=0)\n",
        "  X_2d = tsne.fit_transform(X)\n",
        "  y = np.array(y)\n",
        "\n",
        "  target_names = np.array(['0','1','2','3','4'])\n",
        "  target_ids = range(len(target_names))\n",
        "\n",
        "  plt.figure(figsize=(10, 10))\n",
        "  colors = 'r', 'b','g','c','m'\n",
        "  for i, c, label in zip(target_ids, colors, target_names):\n",
        "      plt.scatter(X_2d[y == i, 0], X_2d[y == i, 1], c=c, label=label)\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "9aFuATdwntaq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# I plot 5000 samples of test set by default\n",
        "num_samples = 5000\n",
        "Plot_TSNE(test_df, num_samples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "3uT_dJTtCAMv",
        "outputId": "d7eb2c39-6766-4dbb-edc0-0a492384b458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Huuum! It's not that bad! :))"
      ],
      "metadata": {
        "id": "Fmb05IQXuEFI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Fine-tuning the Model.\n",
        "\n",
        "Well, in this last step, I'll freeze the encoder parameters and then I'll add a linear layer to it. After that, I'm going to fine-tune the whole model with the Test set.\n",
        "\n",
        "I should mention that, I'll use 90% and 10% of the Test set for fine-tuning and evaluation respectively."
      ],
      "metadata": {
        "id": "LSuP3ynEuVby"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class DSModel(nn.Module):\n",
        "    def __init__(self,premodel,num_classes):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.premodel = premodel\n",
        "        self.num_classes = num_classes\n",
        "        \n",
        "        # freeze pretrained model's parameters\n",
        "        for p in self.premodel.parameters():\n",
        "            p.requires_grad = False\n",
        "        \n",
        "        self.lastlayer = nn.Linear(256,self.num_classes)\n",
        "        \n",
        "    def forward(self,x):\n",
        "        out = self.premodel(x)\n",
        "        out = self.lastlayer(out)\n",
        "        return out"
      ],
      "metadata": {
        "id": "Om1AY34h-02b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def init_model(model, num_class):\n",
        "    \"\"\"Initialize the Classifier, the optimizer and the learning rate scheduler.\n",
        "    \"\"\"\n",
        "    # Instantiate model\n",
        "    model_final = DSModel(model, num_class)\n",
        "\n",
        "    # Create the optimizer\n",
        "    optimizer = torch.optim.AdamW(model_final.parameters(), lr=0.002, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)\n",
        "\n",
        "    return model_final, optimizer"
      ],
      "metadata": {
        "id": "ECTZetJe-715"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Create_Dataloader_final(test_data, batch_size):\n",
        "\n",
        "  # read data\n",
        "  test_ds = pd.read_csv(test_data, header = None)\n",
        "\n",
        "  # shuffle data\n",
        "  test_df = test_ds.sample(frac = 1)\n",
        "\n",
        "  # droped label\n",
        "  x_test = test_df.iloc[:,:-1]\n",
        "  y_test = test_df.iloc[:,-1]\n",
        "\n",
        "  # train, valid, test split\n",
        "  X_train, X_valid, y_train, y_valid = train_test_split(x_test, y_test, test_size=.1, random_state=42)\n",
        "  x_train = np.array(X_train)\n",
        "  x_valid = np.array(X_valid)\n",
        "  y_train = np.array(y_train)\n",
        "  y_valid = np.array(y_valid)\n",
        "\n",
        "\n",
        "  # reshaping data\n",
        "  X_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))\n",
        "  X_valid = np.reshape(x_valid, (x_valid.shape[0],x_valid.shape[1],1))\n",
        "\n",
        "  # Create the DataLoader for our data\n",
        "  X_train = torch.tensor(X_train)\n",
        "  X_valid = torch.tensor(X_valid)\n",
        "  y_train = torch.tensor(y_train)\n",
        "  y_valid = torch.tensor(y_valid)\n",
        "\n",
        "  train_data = TensorDataset(X_train, y_train)\n",
        "  valid_data = TensorDataset(X_valid, y_valid)\n",
        "\n",
        "  train_sampler = RandomSampler(train_data)\n",
        "  valid_sampler = RandomSampler(valid_data)\n",
        "\n",
        "  train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
        "  valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=batch_size)\n",
        "\n",
        "  return train_dataloader, valid_dataloader\n"
      ],
      "metadata": {
        "id": "ud1D9FyX_AXY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def final_evaluate(model, val_dataloader):\n",
        "    \"\"\"After the completion of each training epoch, measure the model's performance\n",
        "    on our validation set.\n",
        "    \"\"\"\n",
        "    model.eval()\n",
        "    loss_sublist = np.array([])\n",
        "    acc_sublist = np.array([])\n",
        "    f1_sublist = np.array([])\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "    with torch.no_grad():\n",
        "       for x,y in val_dataloader:\n",
        "\n",
        "            z = model(x.float())\n",
        "\n",
        "            # Compute the average loss over the validation set.\n",
        "            val_loss = loss_fn(z,y.squeeze(-1).type(torch.LongTensor))\n",
        "\n",
        "            # model's prediction\n",
        "            preds = torch.exp(z.cpu().data)/torch.sum(torch.exp(z.cpu().data))\n",
        "\n",
        "            loss_sublist = np.append(loss_sublist, val_loss.cpu().data)\n",
        "            acc_sublist = np.append(acc_sublist,np.array(np.argmax(preds,axis=1)==y.cpu().data.view(-1)).astype('int'),axis=0)\n",
        "            f1 = f1_score(y.cpu().data.view(-1), np.array(np.argmax(preds,axis=1)), average='weighted')\n",
        "            f1_sublist = np.append(f1_sublist, f1)\n",
        "\n",
        "    return np.mean(loss_sublist), np.mean(acc_sublist), np.mean(f1_sublist)"
      ],
      "metadata": {
        "id": "TzlYB3KbARh9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "def final_train(model, optimizer, ep, train_dataloader_final, val_dataloader_final, evaluation=False):\n",
        "    \"\"\"Train the final model.\n",
        "    \"\"\"\n",
        "\n",
        "    # Start training loop\n",
        "    print(\"Start training...\\n\")\n",
        "    tr_ep_loss = []\n",
        "    tr_ep_acc = []\n",
        "    tr_ep_f1 = []\n",
        "    val_ep_loss = []\n",
        "    val_ep_acc = []\n",
        "    val_ep_f1 = []\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "    # print the headers\n",
        "    print(f\"{'Epoch':^7} | {'Train Loss':^12} | {'Train accuracy':^12} | {'Train F1 score':^12} | {'Val Loss':^10} | {'Val accuuracy':^10} | {'Val F1 score':^10} | {'Elapsed':^9}\")\n",
        "    print(\"-\"*112)\n",
        "\n",
        "    for e in range(1, ep + 1):\n",
        "        # =======================================\n",
        "        #               Training\n",
        "        # =======================================\n",
        "\n",
        "        # Measure the elapsed time of each epoch\n",
        "        t0_epoch, t0_batch = time.time(), time.time()\n",
        "\n",
        "        # Reset tracking variables at the beginning of each epoch\n",
        "        total_loss, batch_loss, batch_counts = 0, 0, 0\n",
        "\n",
        "        # Put the model into the training mode\n",
        "        model.train()\n",
        "        \n",
        "        # For each batch of training data...\n",
        "        loss_sublist = np.array([])\n",
        "        acc_sublist = np.array([])\n",
        "        f1_sublist = np.array([])\n",
        "\n",
        "        for batch, (x, y) in enumerate(train_dataloader_final):\n",
        "            batch_counts +=1\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # model's output\n",
        "            z = model_final(x.float())\n",
        "\n",
        "            # calculate losst\n",
        "            loss = loss_fn(z, y.squeeze(-1).type(torch.LongTensor))\n",
        "            loss.backward()\n",
        "\n",
        "            preds = torch.exp(z.cpu().data)/torch.sum(torch.exp(z.cpu().data))\n",
        "            f1 = f1_score(y.cpu().data.view(-1), np.array(np.argmax(preds,axis=1)), average='weighted')\n",
        "\n",
        "            f1_sublist = np.append(f1_sublist, f1)\n",
        "            loss_sublist = np.append(loss_sublist, loss.cpu().data)\n",
        "            acc_sublist = np.append(acc_sublist,np.array(np.argmax(preds,axis=1)==y.cpu().data.view(-1)).astype('int'),axis=0)\n",
        "            optimizer.step()\n",
        "\n",
        "            # Zero out any previously calculated gradients\n",
        "            model.zero_grad()\n",
        "\n",
        "            # Clip the norm of the gradients to 1.0 to prevent \"exploding gradients\"\n",
        "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "\n",
        "        # Reset batch tracking variables\n",
        "        batch_loss, batch_counts = 0, 0\n",
        "        t0_batch = time.time()\n",
        "\n",
        "        # Calculate the average loss over the entire training data\n",
        "        avg_train_loss = np.mean(loss_sublist)\n",
        "        tr_ep_loss.append(avg_train_loss)\n",
        "        avg_train_accuracy = np.mean(acc_sublist)\n",
        "        tr_ep_acc.append(avg_train_accuracy)\n",
        "        avg_train_f1 = np.mean(f1_sublist)\n",
        "        tr_ep_f1.append(avg_train_f1)\n",
        "\n",
        "        # =======================================\n",
        "        #               Evaluation\n",
        "        # =======================================\n",
        "        if evaluation == True:\n",
        "            # After the completion of each training epoch, measure the model's performance\n",
        "            # on our validation set.\n",
        "            val_loss, val_accuracy, val_f1 = final_evaluate(model, val_dataloader_final)\n",
        "            val_ep_loss.append(val_loss)\n",
        "            val_ep_acc.append(val_accuracy)\n",
        "            val_ep_f1.append(val_f1)\n",
        "\n",
        "            time_elapsed = time.time() - t0_epoch\n",
        "\n",
        "            # Print the results\n",
        "            print(f\"{e:^7} | {avg_train_loss:^12.6f} | {avg_train_accuracy:^14.6} | {avg_train_f1:^14.6} | {val_loss:^10.6f} | {val_accuracy:^13.2f} | {val_f1:^13.2f} | {time_elapsed:^9.2f}\")\n",
        "            print(\"-\"*112)\n",
        "\n",
        "    torch.save(model.state_dict(), '/content/final_model.pt')\n",
        "    print(\"\\n\")\n",
        "    # plot train and valid loss\n",
        "    plt.plot(list(range(len(val_ep_loss))), val_ep_loss, label = \"validation loss\")\n",
        "    plt.plot(list(range(len(tr_ep_loss))), tr_ep_loss, label = \"training loss\")\n",
        "    plt.title('loss')\n",
        "    plt.xlabel('number of epochs')\n",
        "    plt.ylabel('loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    print(\"\\n\")\n",
        "\n",
        "    # plot train and valid accuracy\n",
        "    plt.plot(list(range(len(val_ep_acc))), val_ep_acc, label = \"validation accuracy\")\n",
        "    plt.plot(list(range(len(tr_ep_acc))), tr_ep_acc, label = \"training accuracy\")\n",
        "    plt.title('accuracy')\n",
        "    plt.xlabel('number of epochs')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    print(\"\\n\")\n",
        "\n",
        "    # plot train and valid f1 score\n",
        "    plt.plot(list(range(len(val_ep_f1))), val_ep_f1, label = \"validation F1 Score\")\n",
        "    plt.plot(list(range(len(tr_ep_f1))), tr_ep_f1, label = \"training F1 Score\")\n",
        "    plt.title('F1 Score')\n",
        "    plt.xlabel('number of epochs')\n",
        "    plt.ylabel('F1 Score')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    print(\"\\n\")\n",
        "\n",
        "    print(\"Training complete!\")\n"
      ],
      "metadata": {
        "id": "KTDqRMdYAV0_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create data \n",
        "test_path = \"/content/drive/MyDrive/ECG-data/mitbih_test.csv\"\n",
        "train_dataloader_final, val_dataloader_final = Create_Dataloader_final(test_path, batch_size)"
      ],
      "metadata": {
        "id": "RcZvDoFuAZBE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_final, optimizer2 = init_model(model, 5)"
      ],
      "metadata": {
        "id": "Q-9Ap_iIAc9O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# final training\n",
        "epoch = 10\n",
        "final_train(model_final, optimizer2, epoch, train_dataloader_final, val_dataloader_final, evaluation=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "crPUbFsnBFqv",
        "outputId": "febb286c-d0ee-4cd4-a5b2-a4f596b6c23a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start training...\n",
            "\n",
            " Epoch  |  Train Loss  | Train accuracy | Train F1 score |  Val Loss  | Val accuuracy | Val F1 score |  Elapsed \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   1    |   0.742118   |    0.804588    |    0.822106    |  0.468855  |     0.89      |     0.89      |   10.36  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   2    |   0.425710   |    0.889859    |    0.889302    |  0.368294  |     0.91      |     0.90      |   10.33  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   3    |   0.341997   |    0.91138     |    0.906577    |  0.314219  |     0.92      |     0.92      |   10.35  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   4    |   0.297423   |    0.922292    |    0.914848    |  0.280933  |     0.93      |     0.92      |   10.33  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   5    |   0.269312   |    0.927469    |    0.919447    |  0.253743  |     0.93      |     0.93      |   10.28  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   6    |   0.249178   |    0.934017    |    0.925204    |  0.239958  |     0.94      |     0.93      |   10.30  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   7    |   0.237429   |    0.935184    |    0.927021    |  0.236835  |     0.94      |     0.93      |   10.29  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   8    |   0.226330   |    0.938737    |    0.930268    |  0.235826  |     0.94      |     0.93      |   10.22  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "   9    |   0.219484   |    0.939702    |    0.930976    |  0.225117  |     0.94      |     0.94      |   10.25  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "  10    |   0.214095   |    0.941275    |    0.93259     |  0.220639  |     0.94      |     0.93      |   10.35  \n",
            "----------------------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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yzN+VGGOMz1gQNCV9KlSVwo7P/V2JMcb4jAVBU9J+7HQjtesExphOzIKgKbXdSO06gTGmE7MgOJb0qZC32bqRGmM6LZ8GgYhME5GNIrJZRGY3ss1FIvK9iKwTkdd8WU+rWDdSY0wn57MgEJFg4ElgOjAIuFREBtXbJh24AxivqoOBm3xVT6tZN1JjTCfnyyOCscBmVd2qqhXAG8C59bb5FfCkqh4EUNUcH9bTetaN1BjTifkyCHoAuzxeZ7nLPJ0EnCQiX4jI1yIyraGGROQaEVkuIstzc3N9VG4TrBupMaYT8/fF4hAgHTgNuBR4VkTi6m+kqs+oaqaqZiYnJx/nErFupMaYTs2XQbAb6OnxOtVd5ikLeE9VK1V1G/ADTjC0L6GRThjYdQJjTCfkyyBYBqSLSB8RCQMuAd6rt827OEcDiEgSzqmi9tlPs39tN9Jt/q7EGGO8ymdBoKpVwA3AAmA98KaqrhORe0VkhrvZAiBPRL4HFgG3qWqer2pqk/Spzs/NdnrIGNO5iKr6u4YWyczM1OXLl/tn54+NhMR0uOxN/+zfGGNaSURWqGpmQ+v8fbG4Y+k/FbYttm6kxphOxYKgJawbqTGmE7IgaAnrRmqM6YQsCFrCupEaYzohC4KWsm6kxphOxoKgpawbqTGmk7EgaKnEfhDfBzbZ6SFjTOdgQdAa6daN1BjTeVgQtEb/2m6kX/i7EmOMaTMLgtZI+zEEh9t1AmNMp2BB0BphUU4Y2HUCY0wnYEHQWulnQN4mOLjd35UYY0ybWBC0Vm03UjsqMMZ0cBYErWXdSI0xnYQFQVtYN1JjTCdgQdAW1o3UGNMJWBC0hXUjNcZ0AhYEbWHdSI0xnYAFQVulT7VupMaYDs2CoK36WzdSY0zHZkHQVon9ID7NrhMYYzosC4K2ErFJ7Y0xHZoFgTekT4XKEtj5pb8rMcaYFrMg8Ia0U51upDapvTGmA7Ig8IbabqQ2qb0xpgOyIPCW9Kmw/wfrRmqM6XAsCLzFupEaYzooCwJvsW6kxpgOyoLAW6wbqTGmg/JpEIjINBHZKCKbRWR2A+tniUiuiKxyH1f7sh6fs26kxpgOyGdBICLBwJPAdGAQcKmIDGpg07+r6gj38Zyv6jkurBupMaYD8uURwVhgs6puVdUK4A3gXB/uz//CoiBtvHUjNcZ0KL4Mgh7ALo/XWe6y+i4QkTUiMldEejbUkIhcIyLLRWR5bm6uL2r1nv613Uh3+LsSY4xpFn9fLP4XkKaqw4CPgJca2khVn1HVTFXNTE5OPq4FtljtpPZ2VGCM6SCaFQQi8msR6SqO50VkpYiccYy37QY8/8JPdZfVUdU8VS13Xz4HjG5u4e1WYn+nG6ldJzDGdBDNPSK4SlUPAWcA8cAvgPuP8Z5lQLqI9BGRMOAS4D3PDUTkRI+XM4D1zayn/fLsRlpVfuztjTHGz5obBOL+PBN4WVXXeSxrkKpWATcAC3B+wb+pqutE5F4RmeFudqOIrBOR1cCNwKyWfoB2KX0qVBbDDutGaoxp/0Kaud0KEfkQ6APcISIxQM2x3qSq84H59Zbd6fH8DuCO5pfbQdR1I/0I+k3ydzXGGNOk5h4R/BKYDYxR1RIgFLjSZ1V1dNaN1BjTgTQ3CE4BNqpqvojMBH4PFPiurE7AupEaYzqI5gbBU0CJiAwHbgG2AH/zWVWdgXUjNcZ0EM0NgipVVZw7g59Q1SeBGN+V1Qkk9oe43taN1BjT7jU3CApF5A6cbqPzRCQI5zpBh1FRVcPXW/OO3w5FnKMC60ZqjGnnmhsEFwPlOPcT7MW5OexBn1XlA49+/AOXPfcNizbmHL+d9rdupMaY9q9ZQeD+8n8ViBWRs4EyVe1Q1wiundiPAd1j+M9XVrJqV/7x2WmfUyE4zCarMca0a80dYuIiYCnwM+Ai4BsRudCXhXlbTEQoL141hqSYMK56cRnb9hf7fqdh0dB7vE1faYxp15p7auh3OPcQXKGql+MMMf0H35XlG91iInjpyrEAXD7nG3IKj8NMYulnwP6NkL/T9/syxphWaG4QBKmq58n1vBa8t13pm9yFObPGsL+wgitfWEZhWaVvd5huk9obY9q35v4y/0BEFrhTS84C5lFv6IiOZETPOP46cxQb9hZy7SsrqKg65mgZrVfbjdSuExhj2qnmXiy+DXgGGOY+nlHV231ZmK9NGtCNBy4Yxheb87j1H6upqVHf7Ki2G+nWz6wbqTGmXWruoHOo6lvAWz6s5bi7cHQqOYVl/O8HG+kWE87vz25oSmUv6D8Vlj3ndCO1QeiMMe1Mk0EgIoVAQ38qC6Cq2tUnVR1H103sR86hcp77fBvdu0bwqwl9vb8Tz26kFgTGmHamyVNDqhqjql0beMR0hhAAEBH+cPYgzhp6IvfNX8+73+4+9ptayrqRGmPasQ7Z88fbgoOEP180nHF9E7j1H6tZ/EOu93eSPtW6kRpj2iULAldEaDDPXJ5J/25duO6VFazd7eVRtvtbN1JjTPtkQeCha0QoL101lrioMGa9sJQdeV68+zgpHeJ6WTdSY0y7Y0FQT/euEbx01ViqapQr5ixlf5GXunzWTmpv3UiNMe2MBUED+nfrwvNXjGHvoTKuenEZxeVV3mm4dlL7nV95pz1jjPECC4JGjO4dz5M/H8W67ENc9+pKKqu9cPdxnwlON1K7TmCMaUcsCJowOaM7/3PeEBb/kMvtc9fgTNLWBrXdSL/7BxzY6p0ijTGmjSwIjuHiMb24ZepJvP3tbh74YGPbG5x6D1RXwpxpsHdt29szxpg2siBohhtO78/Mcb14+rMtzPl8W9saO3E4XPUBSDC8eCbs/MY7RRpjTCtZEDSDiHDPjCFMG3wCf5z3Pf9and22BpMHwC8XQFQS/O1cm+DeGONXFgTNFBwkPHLJCMb0TuCWN1fz5eb9bWswrhdctQCS+sPrF8N3c71TqDHGtJAFQQtEhAbz7OWZpCVFcc3LK1iX3ca7j7skw6x50PNkeOtqWPa8dwo1xpgWsCBoodgo5+7jmIgQZr2wjF0HStrWYEQszHwLTvoJzLsZFj8Ibe2dZIwxLeDTIBCRaSKyUUQ2i8jsJra7QERURDJ9WY+3nBgbyd+uGktFVQ1XzFnKgeKKtjUYGgkXvwLDLoZP/gQLfgc1Ppw1zRhjPPgsCEQkGHgSmA4MAi4VkaNmfhGRGODXQIfqPpPePYbnr8hkd34pV724jJKKNt59HBwKP30axv4HfP0kvHcDVHvpjmZjjGmCL48IxgKbVXWrqlYAbwDnNrDdH4EHgDIf1uITmWkJPHbpSNZk5XO9N+4+DgqC6Q/Aab+FVa/CP66Ayg73tRhjOhhfBkEPYJfH6yx3WR0RGQX0VNV5TTUkIteIyHIRWZ6b64O5AtrgJ4NP4I8/HcKijbn89u3v2n73sQicdjtMfxA2/BtevRDKDnmnWGOMaYDfLhaLSBDwMHDLsbZV1WdUNVNVM5OTk31fXAtddnJvfj05nX+syOLPH/7gnUZPvgbOf9aZ5/ilc6C4jd1VjTGmEb4Mgt1AT4/Xqe6yWjHAEOBTEdkOjAPe6ygXjOu7aUo6l47tyROLNvO3r7Z7p9FhF8Elr0HuBnhhOhRkeaddY4zx4MsgWAaki0gfEQkDLgHeq12pqgWqmqSqaaqaBnwNzFDV5T6syWdEhD+eO4QpGd256711zP9uj3caHjANZr4NhXvh+Z/A/k3eadcYY1w+CwJVrQJuABYA64E3VXWdiNwrIjN8tV9/CgkO4vFLRzKqVzw3vbGKr7fmeafhtPEw699QXe4MVpe9yjvtGmMMIG2+uHmcZWZm6vLl7fugIb+kgguf/op9h8r4x7WnMPCErt5pOG8L/O2nUHoQfv4GpP3YO+0aYzo9EVmhqg2eerc7i30gLiqMl64aS1RYMFfMWcru/FLvNJzYzxm5tGsKvHw+bJjvnXaNMQHNgsBHesRF8tJVYympqOaKOUvJL2nj3ce1YnvAle9D98Hw95mw6nXvtGuMCVgWBD408ISuPHt5JjsPlPDLl5ZTVlntnYajE+GK95xTQ+9eC18/5Z12jTEByYLAx8b1TeTRi0ewcudBbnjtW6q8MfcxQHgMXPYPyDgHPpgNn9xng9UZY1rFguA4mD70RO6dMZiF6/fxh3+ua/vdx7VCwuHCF2HkTFj8vzD/NhuszhjTYiH+LiBQ/OKUNPYeKuPJRVuIiQjhtp8MIDTYCzkcHAIznoDIePjycadH0XlPO4PYGWNMM1gQHEe3njGAgyWVPLN4K59syOHeGYP5Uf+ktjcsAlP/CJEJ8PE9UH4IfvYShEW1vW1jTKdnp4aOIxHhf84byvNXZFJRVcPPn/uGG15byZ4CL3QvFYFTb4azH4FNH8Er50NpftvbNcZ0ehYEfjA5ozsf/mYCv5lyEh99v4/Jf/6Mpz/bQkWVF87vZ14JF86BrOXw4tlQuK/tbRpjOjULAj+JCA3m11PSWXjzRH7UL4n739/A9EcX88VmL4wyOuR8587jA1tgzk/g4Pa2t2mM6bQsCPysZ0IUz12RyQuzxlBVo1z23Ddc/+pKstt6N3L/KXD5P6H0gDM+Uc567xRsjOl0LAjaiUkDu7HgpgncMvUkFq53Thc99WkbTxf1HOvchazqDGOd1b7HaDLG+IcFQTsSERrMf012Thedmp7EAx9sYNqji1myqQ2zsnUf7IxPFBELL82ALZ94r2BjTKdgQdAO9UyI4pnLM3nhyjHU1Ci/eH4p172yovWD1yX0gasWQHwavHoRrHvXq/UaYzo2C4J2bNKAbnxw0wRuPeMkFm3MYcqfP+PJRZspr2rFmEUxJ8CV8yBlJMy9Ela85P2CjTEdkgVBOxcRGswNpzuniyaelMyDCzYy7ZElfPZDK04XRcbD5e9Cv9PhXzfCgt9BsZcmzzHGdFgWBB1EanwUT/9iNC9dNRaAK+Ys5T9eXk7WwZKWNRQWDZe8DqMuh6+ehEeGwkd3QlEbrkMYYzo0m6GsAyqvqua5Jdt44pPNKMoNk/rzqwl9CQ8JbllDORtgyUOw9i0IiYDMq+BHN0JMd98Ubozxm6ZmKLMg6MB255fyp39/z/tr95KWGMVdMwYzaUC3lje0fxMsfgi+exOCw2D0lTD+19D1RO8XbYzxCwuCTm7xD7nc/d46tu4vZuqg7tx59iB6JrRiwLm8LbDkYVj9OgSFwOgrYPxNzqxoxpgOzYIgAJRXVfP859t4/OPN1Khy/aT+XDOhLxGhLTxdBHBgG3z+MKx6DSTIme/gxzdDXE/vF26MOS4sCAJIdn4p981bz7zv9tA7MYq7zhnE6QNbec4/f6dzhPDtK87rET+HU2+B+N7eK9gYc1xYEASgzzft58731rI1t5gpGd2565xWni4CKMiCz/8CK/8GWgPDL3ECIaGvd4s2xviMBUGAqqiqYc4X23js401U1yjXndaPayf2a93pIoBD2fDFo7DiRaiuhGEXO4GQ1N+rdRtjvM+CIMDtKSjlT/PWM2/NHnolOKeLJme0oYto4V744jFYPgeqy2HIhTDhVkge4L2ijTFeZUFgAPhi837uem8dm3OKmDywG7OnDyS9e0zrGyzKgS8fg2XPQ2WpMw/ChNugW4b3ijbGeIUFgalTUVXDi19u45GFmyipqGZ4aiwXjE7lnGEpxEeHta7R4v3w1ROw9FmoKIJB58KE/4YThni3eGNMq1kQmKPsLyrn3W938/bK3Xy/5xChwcKkAd04f1Qqpw/sRlhIK0YfKTngDFvxzf9BRSEMPBsm3g4nDvP+BzDGtIgFgWnS+j2HeHtlFu+uyia3sJy4qFBmDE/h/FGpDE+NRURa1mDpQfj6afj6KSgvgAFnOqeMeozyzQcwxhyT34JARKYBjwLBwHOqen+99dcC1wPVQBFwjap+31SbFgS+U1Vdw5LN+3l75W4+XLeX8qoa+iVHc/6oVM4b2YOUuMiWNViaD0ufcY4SyvIh/QznCCG1wf8XjTE+5JcgEJFg4AdgKpAFLAMu9fxFLyJdVfWQ+3wG8J+qOq2pdi0Ijo9DZZW8/90e3lqxm6XbDyACP+qXyPkjU5k25ASiw0Oa31jZITcQnnCOFvpNdgKh18m++9HEyRQAABMdSURBVADGmCP4KwhOAe5W1Z+4r+8AUNX/18j2lwKXq+r0ptq1IDj+duaV8Pa3Wby9cjc7D5QQFRbMtCEncMGoVMb1TSQ4qJmnjsoLYdlz8OXjUJIHfSbCabOh9498+wGMMX4LgguBaap6tfv6F8DJqnpDve2uB24GwoDTVXVTA21dA1wD0KtXr9E7duzwSc2maarKih0HeWtlFv9es4fCsipOjI3gvJE9OH9UKv27dWleQxXFzj0IXzwKxbmQPBAGngUDznJmUAuyaTKM8bZ2HQQe2/8c+ImqXtFUu3ZE0D6UVVazcP0+3lqRxeJN+6muUYb3jOOCUT2a3xW1ogRWv+bMobzjS9BqiDnRubg88ExImwAhrezSaow5Qkc5NRQEHFTV2KbatSBof3IKy3hvVfZRXVEvGJ3KpAHN7IpacgA2fQgb/g2bP4bKEgjvCulTnWBInwoRTf6vYYxpgr+CIATnYvFkYDfOxeKfq+o6j23Sa08Ficg5wF2NFVrLgqB9q98VNT4qlHOGp3DBqFSGNbcramUpbP3MCYUfPnBOHwWFQp8JzpHCgDOha4rvP4wxnYg/u4+eCTyC0310jqreJyL3AstV9T0ReRSYAlQCB4EbPIOiIRYEHYPXuqLWVEPWMtgwzwmGA1ud5T1Gu6eQznbGOGrpvQ7GBBi7ocz41aGySuav2cPbK4/sinrBqFR+MrgFXVFVIXcjbJznBMPuFc7yhH7OkcLAsyF1DAS1cnRVYzoxCwLTbjTUFXXSgG5MzujGpAHdWjbe0aFs2DgfNsyHbYuhphKik+GkaU4o9J0IoS28Cc6YTsqCwLQ7tV1R3/52Nwu/30dOYTlBAqN7xzM5oztTMrrRL7lL84e3KCuAzQudI4VNH0H5IQiNhv6nO6GQfgZEJfj2QxnTjlkQmHatpkZZm13AwvU5LPx+H9/vOQRA78QoJg90QmFMnwRCg5t5f0FVBWxf4oTCxvlQuAck2LlxbeDZzmmkuF4+/ETGtD8WBKZDyc4v5eMNOXy8fh9fbsmjoqqGmIgQJp6UzJSM7pw2IJm4qGaeQqqpgT3fuheb50Puemf5CUPdUDgLug+xi82m07MgMB1WSUUVSzbt5+P1+/hkQw77iyoIDhIye8czJaM7kzO60Te5mXc0A+RtcUNhHuz6BlCI7eUcJfQeDz3HQswJPvs8xviLBYHpFGpqlNVZ+Xy8PoeF6/exYW8hAH2Topmc0Y3JGd3J7B1PSHNPIRXlOPcpbJgPWxdBVZmzPK4XpI51QiF1jHP0EBzqo09lzPFhQWA6payDJXyyIYeF63P4ekseFdU1xEaGctqAZCZndGfiScnERjbzF3hVOexZDbuWQtZS2LUMCrOddSGRzhhIPcccDogu3Xz3wYzxAQsC0+kVlVfx+aZcPvo+h0UbczhQXEFIkDAmLYHJGd2YktGdtKToljVakOUGwzLn557VThdVgLje7hHDWCcgug+xowbTrlkQmIBSXaOs2nWQheudC84/7CsCoF9ytHtdoTujesU1/xRSrcoyJwyylh4OiMI9zrrQKOeoIXXM4YDokuzlT2ZM61kQmIC2M6+Ejzfs4+P1OXyzLY/KaiUuKrTuRrYJJyXTNaIVf82rQsGuI48a9q6BmipnfXyfw9cZeo6FboMhuAUT+hjjRRYExrgKyypZ/IPTC2nRxhwOllQSGuycQhqc0pW+yV3omxRN3+QuJHUJa/l8zZWlkL3qyKOGon3OutBoZ95mz6OG6ETvf0hjGmBBYEwDqmuUlTsPsnD9Phb/sJ8tuUVUVNXUre8aEUKf5C70S46mn0dA9E6MIiK0meMZqUL+zsNHDFlLYe93h48aEvoevs6QOha6DbKjBuMTFgTGNEN1jZKdX8qW3CK25hazdb/7M7eYvYfK6rYTgdT4SPomdaFvshMO/dyQ6N41/NhHERUlkP3t4d5JWUudobbB6aEU39u5GB2f5vHc/RnR1XdfgOnULAiMaaPi8iq27S9mS24RW3KL2eqGxbb9xZRWVtdtFx0WTJ/k6CNCwjmSiCYqrJG/9FXh4HbnqGHPauf5wR3Oz4rCI7eNTHBCIT7tcEDUPo/taTO6mUZZEBjjIzU1yt5DZUccQdQeUWQXlOL5z+vE2Aj61jvN1Dc5mpTYSIKCGjiKUIXSg24wbIf8HYcDIn8H5O863J0VQIKga48jjyA8jyq6dLf5oAOYBYExflBWWc22/cXu6aUiJyDc10XlVXXbRYQGkZboBESvxCiiQoOJCA0mIjSI8NrnIe7zkCB3XTARwUpUeQ6RRVmEF+0i5NBOpDYs8ncc7tpaKyTCuWu6odNO8Wk2FWgnZ0FgTDuiquQWljunmOquQzghkXWwlOqa1v+bDK8LiiC6hlTROyiP1KBcUskhRffRvXof3ar3klS5h8iaoiPeWxbSleKoHpRE96QsOpWy6BTKo1Mpi+5BRZceEN6FIBGCg4RgEYKCnOdBQt3yuvUezz3XH7GNCBIEwXL09i3urWWOqakgsO4JxhxnIkK3rhF06xrBKf2O7D6qqlRWK2VV1ZRVVlNeWUN5VTVllTWUVXr8rHLWlXmsK6+spqyqxvlZty6JbZXprPdYV15VQxnVhFYUkFy1l241++jJPnpW5dKzPJee+d/RSxYRLpVH1HZQu7Bbk8jSZPdnUt3rLE3iENGAd36BBwkEBwldwkPoER9JSmwkKXGRpMY7P1PiIukRF9m6Lr7mKBYExrQjIkJYiBAWEtS6m9xaoaHw2VVZhRTnEHQoi5DCXYQcyiK0MIuehVn0K95NWNFagqtKj2inKrQLpVEplEamUBzVg+LIEymOSqE4IoXCiBRKQ+KoxrmuUl2jVKuzb+e5uss5/FyVgtJKsvNL2Z5XzBeb91NcUX3EPsNCgugRF0lKXAQpsZFOaLgh0SMukhNiI5rf1TeAWRAYE+AaD5+uQP+G36QKJQecaxEFuyB/JyH5u4gp2EVM/k7YvdKZJc5TaJTTsymup3OtItb9Wfv8GBezVZVDpVXszi9ld34p2e7P2uef/ZBLTmH5Ue9L6hJOj/hIejQSFnFRoQF/VGFBYIxpORHnrujoROdu6YaU5teFBPnuzwL3+e6VUHrgyO2DwyA21SMkejtzQ0QlQFQiEplAbFQisSfEMSil4fspyquq2VdQTlZ+Cdn5ZU5YHCwlu6CUDXsL+Xh9DuUeNw0CRIUFe5xuinCPMA6HxQmxEc2fHa+DsiAwxvhGZJzzOGFow+vLi9yg2OVxZOEGxqYPDw/NcRRxejhFJdaFBJEJEJVAeFQCvSIT6BWVAImJkJoAUd0hMh5CwlBVDhRXkJ1fxu78EnbXC4t1uwvIK644Ym9BAjERoUSHBRMdHuI+gokOC6GL+zoqPJguYR7rarcLc153CQ8hyt0+IjSo3R2BWBAYY/wjvAt0y3AeDaksdSYPKj3gnIYqOeA+zzvy+aFs2LvWeV1Z0vj+wmKQqAQSoxJIjExgaG2QxCRCt/i6UCkPTWZvZSRZ5VFkFSm788soKKmgqLyakooqisqrKC6vIq+oou55cXk1FdU1je/bQ5DgBoRHaNR73cUjRA6HSjAZJ3YlJS6yFV920ywIjDHtU6g73EZ87+a/p7L06MAoyXNuzKt77v7M2+wsq3f3djjQ230QEukccUTEuo+uENf18PPwrs7PiFgqQ2MoC4qmOCiaYommUKMorA6luKLGCYsKJzCKyw+HSUlFdd3z3fmlbqg425ZVHh0s9503hMtObsH30UwWBMaYziM0EmJ7OI/mqqpwgyKvgSMO93VZAZQdgkO7oex753n5IdDDv6xD3UeMZ9tBIYfDIrzr4UAJ7wpduh5+fsT6BAiPpSoshmKJpqQm2A2PalLiIrz0RR3JgsAYE9hCwiCmu/NoCVWoKHZCovzQ4XAoK/BYVuCx3H19YOvhZfV7VnmWBcQCscHhdUcdnHYHDL2wTR+3sX0ZY4xpKRHnOkd4F6AFRyCeaqqhvLDx0CgvODJgonwzf4UFgTHG+EtQ8OHeVf4sw5eNi8g0EdkoIptFZHYD628Wke9FZI2IfCwi3r8KYowxpkk+CwIRCQaeBKYDg4BLRWRQvc2+BTJVdRgwF/hfX9VjjDGmYb48IhgLbFbVrapaAbwBnOu5gaouUtXajr9fA6k+rMcYY0wDfBkEPYBdHq+zaPqKyi+B9xtaISLXiMhyEVmem5vrxRKNMca0iwE0RGQmkAk82NB6VX1GVTNVNTM5Ofn4FmeMMZ2cL3sN7QZ6erxOdZcdQUSmAL8DJqrq0UMHGmOM8SlfHhEsA9JFpI+IhAGXAO95biAiI4H/A2aoao4PazHGGNMInwWBqlYBNwALgPXAm6q6TkTuFZEZ7mYPAl2Af4jIKhF5r5HmjDHG+EiHm7NYRHKBHa18exKw34vldHT2fRzJvo/D7Ls4Umf4PnqraoMXWTtcELSFiCxvbPLmQGTfx5Hs+zjMvosjdfbvo130GjLGGOM/FgTGGBPgAi0InvF3Ae2MfR9Hsu/jMPsujtSpv4+AukZgjDHmaIF2RGCMMaYeCwJjjAlwARMEx5obIVCISE8RWeTOA7FORH7t75raAxEJFpFvReTf/q7F30QkTkTmisgGEVkvIqf4uyZ/EZHfuP9O1orI6yLim0mD/SwggqCZcyMEiirgFlUdBIwDrg/g78LTr3HugDfwKPCBqg4EhhOg34uI9ABuxJkzZQgQjDNUTqcTEEFAM+ZGCBSqukdVV7rPC3H+kbdywtXOQURSgbOA5/xdi7+JSCwwAXgeQFUrVDXfv1X5VQgQKSIhQBSQ7ed6fCJQgqClcyMEBBFJA0YC3/i3Er97BPhvoMbfhbQDfYBc4AX3VNlzIhLt76L8QVV3Aw8BO4E9QIGqfujfqnwjUILA1CMiXYC3gJtU9ZC/6/EXETkbyFHVFf6upZ0IAUYBT6nqSKAYCMhraiISj3PmoA+QAkS7c6d0OoESBM2aGyFQiEgoTgi8qqpv+7sePxsPzBCR7TinDE8XkVf8W5JfZQFZqlp7lDgXJxgC0RRgm6rmqmol8DbwIz/X5BOBEgTHnBshUIiI4Jz/Xa+qD/u7Hn9T1TtUNVVV03D+v/hEVTvlX33Noap7gV0iMsBdNBn43o8l+dNOYJyIRLn/bibTSS+c+3KGsnZDVatEpHZuhGBgjqqu83NZ/jIe+AXwnYiscpf9VlXn+7Em0778F/Cq+0fTVuBKP9fjF6r6jYjMBVbi9Lb7lk461IQNMWGMMQEuUE4NGWOMaYQFgTHGBDgLAmOMCXAWBMYYE+AsCIwxJsBZEJiAIyKfiojPJyIXkRvd0Ttf9fW+6u33bhG59Xju03RsAXEfgTHeIiIhqlrVzM3/E5iiqlm+rMmYtrIjAtMuiUia+9f0s+548B+KSKS7ru4vehFJcoeHQERmici7IvKRiGwXkRtE5GZ38LSvRSTBYxe/EJFV7jjzY933R4vIHBFZ6r7nXI923xORT4CPG6j1ZredtSJyk7vsaaAv8L6I/Kbe9sEi8qCILBORNSLyH+7y00RksYjMc+fOeFpEgtx1l4rId+4+HvBoa5qIrBSR1SLiWdsg93vaKiI3eny+ee62a0Xk4rb8NzKdiKrawx7t7gGk4dzNOcJ9/SYw033+Kc4Y8QBJwHb3+SxgMxADJAMFwLXuur/gDLBX+/5n3ecTgLXu8//x2Ecc8AMQ7babBSQ0UOdo4Dt3uy7AOmCku247kNTAe64Bfu8+DweW4wxsdhpQhhMgwcBHwIU4A57tdD9TCPAJ8FP39S6gj9tWgvvzbuBLt+0kIA8IBS6o/dzudrH+/u9sj/bxsFNDpj3bpqq1w2CswAmHY1mkzjwLhSJSAPzLXf4dMMxju9cBVHWxiHQVkTjgDJwB6GrPr0cAvdznH6nqgQb292PgHVUtBhCRt4FTcYYjaMwZwDARudB9HQukAxXAUlXd6rb1utt+JfCpqua6y1/FCbBqYLGqbnM/i2d981S1HCgXkRygu/sd/Nk9ovi3qi5pokYTQCwITHtW7vG8Goh0n1dx+LRm/akDPd9T4/G6hiP/f68/tooCAlygqhs9V4jIyTjDMXuLAP+lqgvq7ee0RupqjfrfXYiq/iAio4AzgT+JyMeqem8r2zediF0jMB3RdpxTMuCcOmmNiwFE5Mc4E44U4AxK+F/uSJOIyMhmtLME+Kk7QmU0cJ67rCkLgOvc4cARkZM8Jn8Z646SG+TW+DmwFJjoXg8JBi4FPgO+BiaISB+3nYT6O/IkIilAiaq+AjxI4A4vbeqxIwLTET0EvCki1wDzWtlGmYh8i3Pu/Cp32R9xZitb4/4i3gac3VQjqrpSRF7E+WUN8JyqNnVaCJwpMdOAlW7o5OKc8wdnyPQngP7AIpzTTjUiMtt9LTinff4J4H4Hb7v15gBTm9jvUOBBEanBOd103THqNAHCRh81pp1wTw3dqqpNho8x3manhowxJsDZEYExxgQ4OyIwxpgAZ0FgjDEBzoLAGGMCnAWBMcYEOAsCY4wJcP8fdrlZ+OUAaxIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Training complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As it's shown, the accuracy and F1_score are about **94%**."
      ],
      "metadata": {
        "id": "3t9q8wEixqoB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. Conclusion.\n",
        "\n",
        "In this work, I've tried to implement SSCL with the MIT-BIH dataset. \n",
        "\n",
        "This method is highly dependent on the number of unlabeled data. The more unlabeled data we have, the better model we've got.\n",
        "\n",
        "\n",
        "This method is really suitable for generalization because after training the encoder we can fine-tune it with different datasets.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "7ycVyTbaxkCk"
      }
    }
  ]
}